Hadoop分布式集群环境搭建

分布式环境搭建之环境介绍

之前我们已经介绍了如何在单机上搭建伪分布式的Hadoop环境，而在实际情况中，肯定都是多机器多节点的分布式集群环境，所以本文将简单介绍一下如何在多台机器上搭建Hadoop的分布式环境。

我这里准备了三台机器，IP地址如下：

192.168.77.128
192.168.77.130
192.168.77.134

首先在这三台机器上编辑/etc/hosts配置文件，修改主机名以及配置其他机器的主机名

[root@localhost ~]# vim /etc/hosts  # 三台机器都需要操作
192.168.77.128 hadoop000
192.168.77.130 hadoop001
192.168.77.134 hadoop002
[root@localhost ~]# reboot

三台机器在集群中所担任的角色：

hadoop000作为NameNode、DataNode、ResourceManager、NodeManager
hadoop001作为DataNode、NodeManager
hadoop002也是作为DataNode、NodeManager

配置ssh免密码登录

集群之间的机器需要相互通信，所以我们得先配置免密码登录。在三台机器上分别运行如下命令，生成密钥对：

[root@hadoop000 ~]# ssh-keygen -t rsa  # 三台机器都需要执行这个命令生成密钥对
Generating public/private rsa key pair.
Enter file in which to save the key (/root/.ssh/id_rsa): 
Enter passphrase (empty for no passphrase): 
Enter same passphrase again: 
Your identification has been saved in /root/.ssh/id_rsa.
Your public key has been saved in /root/.ssh/id_rsa.pub.
The key fingerprint is:
0d:00:bd:a3:69:b7:03:d5:89:dc:a8:a2:ca:28:d6:06 root@hadoop000
The key's randomart image is:
+--[ RSA 2048]----+
|    .o.          |
|      ..         |
|     . *..       |
|      B +o       |
|     = .S .      |
| E. * .          |
| .oo o .         |
|=. o  o          |
|*..    .         |
+-----------------+
[root@hadoop000 ~]# ls .ssh/
authorized_keys  id_rsa  id_rsa.pub  known_hosts
[root@hadoop000 ~]#

以hadoop000为主，执行以下命令，分别把公钥拷贝到其他机器上：

[root@hadoop000 ~]# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop000
[root@hadoop000 ~]# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop001
[root@hadoop000 ~]# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop002

注：其他两台机器也需要执行以上这三条命令。

拷贝完成之后，测试能否正常进行免密登录：

[root@hadoop000 ~]# ssh hadoop000
Last login: Mon Apr  2 17:20:02 2018 from localhost
[root@hadoop000 ~]# ssh hadoop001
Last login: Tue Apr  3 00:49:59 2018 from 192.168.77.1
[root@hadoop001 ~]# 登出
Connection to hadoop001 closed.
[root@hadoop000 ~]# ssh hadoop002
Last login: Tue Apr  3 00:50:03 2018 from 192.168.77.1
[root@hadoop002 ~]# 登出
Connection to hadoop002 closed.
[root@hadoop000 ~]# 登出
Connection to hadoop000 closed.
[root@hadoop000 ~]#

如上，hadoop000机器已经能够正常免密登录其他两台机器，那么我们的配置就成功了。

安装JDK

到Oracle官网拿到JDK的下载链接，我这里用的是JDK1.8，地址如下：

http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html

使用wget命令将JDK下载到/usr/local/src/目录下，我这里已经下载好了：

[root@hadoop000 ~]# cd /usr/local/src/
[root@hadoop000 /usr/local/src]# ls
jdk-8u151-linux-x64.tar.gz
[root@hadoop000 /usr/local/src]#

解压下载的压缩包，并将解压后的目录移动到/usr/local/目录下：

[root@hadoop000 /usr/local/src]# tar -zxvf jdk-8u151-linux-x64.tar.gz
[root@hadoop000 /usr/local/src]# mv ./jdk1.8.0_151 /usr/local/jdk1.8

编辑/etc/profile文件配置环境变量：

[root@hadoop000 ~]# vim /etc/profile  # 增加如下内容
JAVA_HOME=/usr/local/jdk1.8/
JAVA_BIN=/usr/local/jdk1.8/bin
JRE_HOME=/usr/local/jdk1.8/jre
PATH=$PATH:/usr/local/jdk1.8/bin:/usr/local/jdk1.8/jre/bin
CLASSPATH=/usr/local/jdk1.8/jre/lib:/usr/local/jdk1.8/lib:/usr/local/jdk1.8/jre/lib/charsets.jar

export PATH=$PATH:/usr/local/mysql/bin/

使用source命令加载配置文件，让其生效，生效后执行java -version命令即可看到JDK的版本：

[root@hadoop000 ~]# source /etc/profile
[root@hadoop000 ~]# java -version
java version "1.8.0_151"
Java(TM) SE Runtime Environment (build 1.8.0_151-b12)
Java HotSpot(TM) 64-Bit Server VM (build 25.151-b12, mixed mode)
[root@hadoop000 ~]#

在hadoop000上安装完JDK后，通过rsync命令，将JDK以及配置文件都同步到其他机器上：

[root@hadoop000 ~]# rsync -av /usr/local/jdk1.8 hadoop001:/usr/local
[root@hadoop000 ~]# rsync -av /usr/local/jdk1.8 hadoop002:/usr/local
[root@hadoop000 ~]# rsync -av /etc/profile hadoop001:/etc/profile
[root@hadoop000 ~]# rsync -av /etc/profile hadoop002:/etc/profile

同步完成后，分别在两台机器上source配置文件，让环境变量生效，生效后再执行java -version命令测试JDK是否已安装成功。

Hadoop配置及分发

下载Hadoop 2.6.0-cdh5.7.0的tar.gz包并解压：

[root@hadoop000 ~]# cd /usr/local/src/
[root@hadoop000 /usr/local/src]# wget http://archive.cloudera.com/cdh5/cdh/5/hadoop-2.6.0-cdh5.7.0.tar.gz
[root@hadoop000 /usr/local/src]# tar -zxvf hadoop-2.6.0-cdh5.7.0.tar.gz -C /usr/local/

注：如果在Linux上下载得很慢的话，可以在windows的迅雷上使用这个链接进行下载。然后再上传到Linux中，这样就会快一些。

解压完后，进入到解压后的目录下，可以看到hadoop的目录结构如下：

[root@hadoop000 /usr/local/src]# cd /usr/local/hadoop-2.6.0-cdh5.7.0/
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0]# ls
bin             cloudera  examples             include  libexec      NOTICE.txt  sbin   src
bin-mapreduce1  etc       examples-mapreduce1  lib      LICENSE.txt  README.txt  share
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0]#

简单说明一下其中几个目录存放的东西：

以上就算是把hadoop给安装好了，接下来就是编辑配置文件，把JAVA_HOME配置一下：

[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0]# cd etc/
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc]# cd hadoop
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# vim hadoop-env.sh
export JAVA_HOME=/usr/local/jdk1.8/  # 根据你的环境变量进行修改
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]#

然后将Hadoop的安装目录配置到环境变量中，方便之后使用它的命令：

[root@hadoop000 ~]# vim ~/.bash_profile  # 增加以下内容
export HADOOP_HOME=/usr/local/hadoop-2.6.0-cdh5.7.0/
export PATH=$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$PATH
[root@localhost ~]# source !$
source ~/.bash_profile
[root@localhost ~]#

接着分别编辑core-site.xml以及hdfs-site.xml配置文件：

[root@hadoop000 ~]# cd $HADOOP_HOME
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0]# cd etc/hadoop
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# vim core-site.xml   # 增加如下内容
<configuration>
    <property>
        <name>fs.default.name</name>
        <value>hdfs://hadoop000:8020</value>  # 指定默认的访问地址以及端口号
    </property>
</configuration>
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# vim hdfs-site.xml  # 增加如下内容
<configuration>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>/data/hadoop/app/tmp/dfs/name</value>  # namenode临时文件所存放的目录
    </property>
    <property>
        <name>dfs.datanode.data.dir</name>
        <value>/data/hadoop/app/tmp/dfs/data</value>  # datanode临时文件所存放的目录
    </property>
</configuration>
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# mkdir -p /data/hadoop/app/tmp/dfs/name
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# mkdir -p /data/hadoop/app/tmp/dfs/data

接下来还需要编辑yarn-site.xml配置文件：

[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# vim yarn-site.xml  # 增加如下内容
<configuration>
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <name>yarn.resourcemanager.hostname</name>
        <value>hadoop000</value>
    </property>
</configuration>
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]#

拷贝并编辑MapReduce的配置文件：

[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# cp mapred-site.xml.template mapred-site.xml
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# vim !$   # 增加如下内容
<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
</configuration>
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]#

最后是配置从节点的主机名，如果没有配置主机名的情况下就使用IP：

[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# vim slaves
hadoop000
hadoop001
hadoop002
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]#

到此为止，我们就已经在hadoop000上搭建好了我们主节点（master）的Hadoop集群环境，但是还有其他两台作为从节点（slave）的机器没配置Hadoop环境，所以接下来需要把hadoop000上的Hadoop安装目录以及环境变量配置文件分发到其他两台机器上，分别执行如下命令：

[root@hadoop000 ~]# rsync -av /usr/local/hadoop-2.6.0-cdh5.7.0/ hadoop001:/usr/local/hadoop-2.6.0-cdh5.7.0/
[root@hadoop000 ~]# rsync -av /usr/local/hadoop-2.6.0-cdh5.7.0/ hadoop002:/usr/local/hadoop-2.6.0-cdh5.7.0/
[root@hadoop000 ~]# rsync -av ~/.bash_profile hadoop001:~/.bash_profile
[root@hadoop000 ~]# rsync -av ~/.bash_profile hadoop002:~/.bash_profile

分发完成之后到两台机器上分别执行source命令以及创建临时目录：

[root@hadoop001 ~]# source .bash_profile
[root@hadoop001 ~]# mkdir -p /data/hadoop/app/tmp/dfs/name
[root@hadoop001 ~]# mkdir -p /data/hadoop/app/tmp/dfs/data
[root@hadoop002 ~]# source .bash_profile
[root@hadoop002 ~]# mkdir -p /data/hadoop/app/tmp/dfs/name
[root@hadoop002 ~]# mkdir -p /data/hadoop/app/tmp/dfs/data

Hadoop格式化及启停

对NameNode做格式化，只需要在hadoop000上执行即可：

[root@hadoop000 ~]# hdfs namenode -format

格式化完成之后，就可以启动Hadoop集群了：

[root@hadoop000 ~]# start-all.sh 
This script is Deprecated. Instead use start-dfs.sh and start-yarn.sh
18/04/02 20:10:59 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Starting namenodes on [hadoop000]
hadoop000: starting namenode, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/hadoop-root-namenode-hadoop000.out
hadoop000: starting datanode, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/hadoop-root-datanode-hadoop000.out
hadoop001: starting datanode, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/hadoop-root-datanode-hadoop001.out
hadoop002: starting datanode, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/hadoop-root-datanode-hadoop002.out
Starting secondary namenodes [0.0.0.0]
The authenticity of host '0.0.0.0 (0.0.0.0)' can't be established.
ECDSA key fingerprint is 4d:5a:9d:31:65:75:30:47:a3:9c:f5:56:63:c4:0f:6a.
Are you sure you want to continue connecting (yes/no)? yes  # 输入yes即可
0.0.0.0: Warning: Permanently added '0.0.0.0' (ECDSA) to the list of known hosts.
0.0.0.0: starting secondarynamenode, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/hadoop-root-secondarynamenode-hadoop000.out
18/04/02 20:11:21 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
starting yarn daemons
starting resourcemanager, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/yarn-root-resourcemanager-hadoop000.out
hadoop001: starting nodemanager, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/yarn-root-nodemanager-hadoop001.out
hadoop002: starting nodemanager, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/yarn-root-nodemanager-hadoop002.out
hadoop000: starting nodemanager, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/yarn-root-nodemanager-hadoop000.out
[root@hadoop000 ~]# jps  # 查看是否有以下几个进程
6256 Jps
5538 DataNode
5843 ResourceManager
5413 NameNode
5702 SecondaryNameNode
5945 NodeManager
[root@hadoop000 ~]#

到另外两台机器上检查进程： hadoop001：

[root@hadoop001 ~]# jps
3425 DataNode
3538 NodeManager
3833 Jps
[root@hadoop001 ~]#

hadoop002：

[root@hadoop002 ~]# jps
3171 DataNode
3273 NodeManager
3405 Jps
[root@hadoop002 ~]#

各机器的进程检查完成，并且确定没有问题后，在浏览器上访问主节点的50070端口，例如：192.168.77.128:50070。会访问到如下页面：

点击 ”Live Nodes“ 查看存活的节点：

如上，可以访问50070端口就代表集群中的HDFS是正常的。

接下来我们还需要访问主节点的8088端口，这是YARN的web服务端口，例如：192.168.77.128:8088。如下：

点击 “Active Nodes” 查看存活的节点：

好了，到此为止我们的Hadoop分布式集群环境就搭建完毕了，就是这么简单。那么启动了集群之后要如何关闭集群呢？也很简单，在主节点上执行如下命令即可：

[root@hadoop000 ~]# stop-all.sh

分布式环境下HDFS及YARN的使用

实际上分布式环境下HDFS及YARN的使用和伪分布式下是一模一样的，例如HDFS的shell命令的使用方式依旧是和伪分布式下一样的。例如：

[root@hadoop000 ~]# hdfs dfs -ls /
[root@hadoop000 ~]# hdfs dfs -mkdir /data
[root@hadoop000 ~]# hdfs dfs -put ./test.sh /data
[root@hadoop000 ~]# hdfs dfs -ls /
Found 1 items
drwxr-xr-x   - root supergroup          0 2018-04-02 20:29 /data
[root@hadoop000 ~]# hdfs dfs -ls /data
Found 1 items
-rw-r--r--   3 root supergroup         68 2018-04-02 20:29 /data/test.sh
[root@hadoop000 ~]#

在集群中的其他节点也可以访问HDFS，而且在集群中HDFS是共享的，所有节点访问的数据都是一样的。例如我在hadoop001节点中，上传一个目录：

[root@hadoop001 ~]# hdfs dfs -ls /
Found 1 items
drwxr-xr-x   - root supergroup          0 2018-04-02 20:29 /data
[root@hadoop001 ~]# hdfs dfs -put ./logs /
[root@hadoop001 ~]# hdfs dfs -ls /
drwxr-xr-x   - root supergroup          0 2018-04-02 20:29 /data
drwxr-xr-x   - root supergroup          0 2018-04-02 20:31 /logs
[root@hadoop001 ~]#

然后再到hadoop002上查看：

[root@hadoop002 ~]# hdfs dfs -ls /
Found 2 items
drwxr-xr-x   - root supergroup          0 2018-04-02 20:29 /data
drwxr-xr-x   - root supergroup          0 2018-04-02 20:31 /logs
[root@hadoop002 ~]#

可以看到，不同的节点，访问的数据也是一样的。由于和伪分布式下的操作是一样的，我这里就不再过多演示了。

简单演示了HDFS的操作之后，我们再来运行一下Hadoop自带的案例，看看YARN上是否能获取到任务的执行信息。随便在一个节点上执行如下命令：

[root@hadoop002 ~]# cd /usr/local/hadoop-2.6.0-cdh5.7.0/share/hadoop/mapreduce
[root@hadoop002 /usr/local/hadoop-2.6.0-cdh5.7.0/share/hadoop/mapreduce]# hadoop jar ./hadoop-mapreduce-examples-2.6.0-cdh5.7.0.jar pi 3 4
[root@hadoop002 ~]#

申请资源：

执行任务：

然而我这不幸的执行失败（容我喊一句当妈的撕高达）：

能咋办，只能排错咯，查看到命令行终端的报错信息如下：

Note: System times on machines may be out of sync. Check system time and time zones.
    at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
    at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
    at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
    at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
    at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.instantiateException(SerializedExceptionPBImpl.java:168)
    at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.deSerialize(SerializedExceptionPBImpl.java:106)
    at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$Container.launch(ContainerLauncherImpl.java:159)
    at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$EventProcessor.run(ContainerLauncherImpl.java:379)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

18/04/03 04:32:17 INFO mapreduce.Job: Task Id : attempt_1522671083370_0001_m_000002_0, Status : FAILED
Container launch failed for container_1522671083370_0001_01_000004 : org.apache.hadoop.yarn.exceptions.YarnException: Unauthorized request to start container. 
This token is expired. current time is 1522701136752 found 1522673393827
Note: System times on machines may be out of sync. Check system time and time zones.
    at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
    at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
    at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
    at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
    at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.instantiateException(SerializedExceptionPBImpl.java:168)
    at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.deSerialize(SerializedExceptionPBImpl.java:106)
    at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$Container.launch(ContainerLauncherImpl.java:159)
    at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$EventProcessor.run(ContainerLauncherImpl.java:379)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

18/04/03 04:32:18 INFO mapreduce.Job: Task Id : attempt_1522671083370_0001_m_000001_1, Status : FAILED
Container launch failed for container_1522671083370_0001_01_000005 : org.apache.hadoop.yarn.exceptions.YarnException: Unauthorized request to start container. 
This token is expired. current time is 1522701157769 found 1522673395895
Note: System times on machines may be out of sync. Check system time and time zones.
    at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
    at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
    at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
    at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
    at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.instantiateException(SerializedExceptionPBImpl.java:168)
    at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.deSerialize(SerializedExceptionPBImpl.java:106)
    at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$Container.launch(ContainerLauncherImpl.java:159)
    at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$EventProcessor.run(ContainerLauncherImpl.java:379)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

18/04/03 04:32:20 INFO mapreduce.Job: Task Id : attempt_1522671083370_0001_m_000001_2, Status : FAILED
Container launch failed for container_1522671083370_0001_01_000007 : org.apache.hadoop.yarn.exceptions.YarnException: Unauthorized request to start container. 
This token is expired. current time is 1522701159832 found 1522673397934
Note: System times on machines may be out of sync. Check system time and time zones.
    at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
    at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
    at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
    at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
    at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.instantiateException(SerializedExceptionPBImpl.java:168)
    at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.deSerialize(SerializedExceptionPBImpl.java:106)
    at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$Container.launch(ContainerLauncherImpl.java:159)
    at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$EventProcessor.run(ContainerLauncherImpl.java:379)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

18/04/03 04:32:23 INFO mapreduce.Job:  map 33% reduce 100%
18/04/03 04:32:24 INFO mapreduce.Job:  map 100% reduce 100%
18/04/03 04:32:24 INFO mapreduce.Job: Job job_1522671083370_0001 failed with state FAILED due to: Task failed task_1522671083370_0001_m_000001
Job failed as tasks failed. failedMaps:1 failedReduces:0

18/04/03 04:32:24 INFO mapreduce.Job: Counters: 12
    Job Counters 
        Killed map tasks=2
        Launched map tasks=2
        Other local map tasks=4
        Data-local map tasks=3
        Total time spent by all maps in occupied slots (ms)=10890
        Total time spent by all reduces in occupied slots (ms)=0
        Total time spent by all map tasks (ms)=10890
        Total vcore-seconds taken by all map tasks=10890
        Total megabyte-seconds taken by all map tasks=11151360
    Map-Reduce Framework
        CPU time spent (ms)=0
        Physical memory (bytes) snapshot=0
        Virtual memory (bytes) snapshot=0
Job Finished in 23.112 seconds
java.io.FileNotFoundException: File does not exist: hdfs://hadoop000:8020/user/root/QuasiMonteCarlo_1522701120069_2085123424/out/reduce-out
    at org.apache.hadoop.hdfs.DistributedFileSystem$19.doCall(DistributedFileSystem.java:1219)
    at org.apache.hadoop.hdfs.DistributedFileSystem$19.doCall(DistributedFileSystem.java:1211)
    at org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81)
    at org.apache.hadoop.hdfs.DistributedFileSystem.getFileStatus(DistributedFileSystem.java:1211)
    at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1750)
    at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1774)
    at org.apache.hadoop.examples.QuasiMonteCarlo.estimatePi(QuasiMonteCarlo.java:314)
    at org.apache.hadoop.examples.QuasiMonteCarlo.run(QuasiMonteCarlo.java:354)
    at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:70)
    at org.apache.hadoop.examples.QuasiMonteCarlo.main(QuasiMonteCarlo.java:363)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at org.apache.hadoop.util.ProgramDriver$ProgramDescription.invoke(ProgramDriver.java:71)
    at org.apache.hadoop.util.ProgramDriver.run(ProgramDriver.java:144)
    at org.apache.hadoop.examples.ExampleDriver.main(ExampleDriver.java:74)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at org.apache.hadoop.util.RunJar.run(RunJar.java:221)
    at org.apache.hadoop.util.RunJar.main(RunJar.java:136)

虽然报了一大串的错误信息，但是从报错信息中，可以看到第一句是System times on machines may be out of sync. Check system time and time zones.，这是说机器上的系统时间可能不同步。让我们检查系统时间和时区。然后我就检查了集群中所有机器的时间，的确是不同步的。那么要如何同步时间呢？那就要使用到ntpdate命令了，在所有机器上安装ntp包，并执行同步时间的命令，如下：

[root@hadoop000 ~]# yum install -y ntp
[root@hadoop000 ~]# ntpdate -u ntp.api.bz

完成之后再次执行之前的命令，这次任务执行成功：

将Hadoop项目运行在Hadoop集群之上

在这之前用Hadoop写了一个统计日志数据的小项目，现在既然我们的集群搭建成功了，那么当然是得拿上来跑一下看看。首先将日志文件以及jar包上传到服务器上：

[root@hadoop000 ~]# ls
10000_access.log hadoop-train-1.0-jar-with-dependencies.jar
[root@hadoop000 ~]#

把日志文件put到HDFS文件系统中：

[root@hadoop000 ~]# hdfs dfs -put ./10000_access.log /
[root@hadoop000 ~]# hdfs dfs -ls /
Found 5 items
-rw-r--r--   3 root supergroup    2769741 2018-04-02 21:13 /10000_access.log
drwxr-xr-x   - root supergroup          0 2018-04-02 20:29 /data
drwxr-xr-x   - root supergroup          0 2018-04-02 20:31 /logs
drwx------   - root supergroup          0 2018-04-02 20:39 /tmp
drwxr-xr-x   - root supergroup          0 2018-04-02 20:39 /user
[root@hadoop000 ~]#

执行以下命令，将项目运行在Hadoop集群之上：

[root@hadoop000 ~]# hadoop jar ./hadoop-train-1.0-jar-with-dependencies.jar org.zero01.hadoop.project.LogApp /10000_access.log /browserout

到YARN上查看任务执行时的信息：申请资源：

执行任务：

任务执行成功：

查看输出文件内容：

[root@hadoop000 ~]# hdfs dfs -ls /browserout
Found 2 items
-rw-r--r--   3 root supergroup          0 2018-04-02 21:22 /browserout/_SUCCESS
-rw-r--r--   3 root supergroup         56 2018-04-02 21:22 /browserout/part-r-00000
[root@hadoop000 ~]# hdfs dfs -text /browserout/part-r-00000
Chrome  2775
Firefox 327
MSIE    78
Safari  115
Unknown 6705
[root@hadoop000 ~]#

处理结果没有问题，到此为止，我们的测试也完成了，接下来就可以愉快的使用Hadoop集群来帮我们处理数据了（当然代码你还是得写的）。

从整个Hadoop分布式集群环境的搭建到使用的过程中，可以看到除了搭建与伪分布式有些许区别外，在使用上基本是一模一样的。所以也建议在学习的情况下使用伪分布式环境即可，毕竟集群的环境比较复杂，容易出现节点间通信障碍的问题。如果卡在这些问题上，导致学习不成还气得不行就得不偿失了233。